Article Per Year (5 Year)
p-Index From 2021 - 2026
7.183
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi INOVTEK Polbeng - Seri Informatika Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Data Science Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Journal of Engineering and Information Technology for Community Service Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Computer Science Jurnal Informatika: Jurnal Pengembangan IT Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science International Journal of Informatics and Data Science
Isnain, Auliya Rahman
Fakultas Teknik Dan Ilmu Komputer, Universitas Teknokrat Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 52 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 52 Documents
Search

 2   3   4   5   6 
Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis Styawati Styawati; Auliya Rahman Isnain; Nirwana Hendrastuty; Lili Andraini
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 1 (2021): JPIT, Januari 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i1.3245

Abstract

Twitter is a social media that is widely used by the public. Twitter social media can be used to express opinions or opinions about an object. This shows that there is a huge opportunity for data sources, so they can be used for sentiment analysis. There are many algorithms for performing sentiment analysis, including Support Vector Machine (SVM) and Naive Bayes (NB). Because of the many opinions regarding the performance of the two methods, the researcher is interested in classifying the data using the SVM and NB methods. The data used in this study is data on public opinion regarding the Covid-19 vaccination policy. The first classification process is carried out by the SVM method using various kernels. After getting the highest accuracy result, then the accuracy result is compared with the accuracy value from the NB method classification results.
Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine Styawati Styawati; Nirwana Hendrastuty; Auliya Rahman Isnain
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 3 (2021): JPIT, September 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i3.2870

Abstract

Program kartu prakerja diluncurkan pada tahun 2020 melalui peraturan Presiden Nomor 36 tahun 2020 tentang Pengembangan Kompetensi Kerja melalui Program Kartu Pra-Kerja. Maraknya pembahasan program kartu prakerja di twitter membuat penulis tertarik untuk menganalisa sentimen  masyarakat Indonesia terhadap Program kartu Prakerja tentang trobosan upaya pemerintah mengatasi penganguran dan korban PHK tenaga kerja dengan keyword “prakerja”. Sentimen yang digunakan adalah positif, negatif, dan netral. Metode yang digunakan untuk menganalisis opini masyarakat dengan data yang diperoleh pada sosial media twitter menggunakan Support Vector Machine (SVM). Sedangkan untuk mengukur kinerja klasifikasi SVM menggunakan metode Confusion Matrix. Pada penelitian ini dilakukan perbandingan dua kernel yaitu linear dengan RBF. Hasil evaluasi yang dilakukan pada nilai akurasi kernel linear 98.67%, precission 98%, recall 99%, dan F1-Score 98%, sedangkan pada nilai akurasi kernel RBF 98.34%, precission 97%, recall 98%, F1-Score 98%, dapat disimpulkan bahwa sentimen masyarakat dari pengguna twitter terhadap program kartu prakerja dimasa pandemi lebih condong ke netral sebesar 98,34%. Berdasarkan hasil evaluasi yang dilakukan pada nilai akurasi kernel linear menghasilkan nilai akurasi 98.67%, sedangkan kernel RBF menghasilkan akurasi 98.34%. Maka dari sisi akurasi kernel linear lebih akurat dari pada kernel RBF.
Penerapan Metode TOPSIS untuk Pemilihan Distributor Terbaik Agung Deni Wahyudi; Auliya Rahman Isnain
Journal of Artificial Intelligence and Technology Information Vol. 1 No. 2 (2023): Volume 1 Number 2 June 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v1i2.41

Abstract

Sistem Pendukung Keputusan (SPK) merupakan sebuah bagian dari sistem informasi yang memanfaatkan teknologi informasi yang berbasis pengetahuan yang digunakan dalam pengambilan sebuah keputusan. Tujuan penelitian ini untuk melakukan pemilihan distributor terbaik menggunakan metode TOPSIS dengan menggunakan kriteria harga, pengiriman, kualitas, bentuk kemasan, serta retur. Hasil perhitungan manual menggunakan metode TOPSIS dalam pemilihan distributor terbaik, untuk peringkat 1 didapatkan oleh PT Agung Jaya dengan nilai sebesar 0,619, peringkat 2 didapatkan oleh CV Bintang Samudra dengan nilai sebesar 0,582, peringkat 3 didapatkan oleh CV Putra Mandiri dengan nilai sebesar 0,575. Dari keseluruhan kriteria Model DeLone dan McLeon untuk kesuksesan sistem informasi untuk aplikasi pemiihan distributor terbaik mendapatkan hasil Baik sebesar 82,5%.
Rancang Bangun Sistem Informasi Bimbingan Konseling Berbasis Web Pada SMA N 01 Sindang Danau Bakti Eka Putra; Ade Surahman; Auliya Rahman Isnain
Journal of Artificial Intelligence and Technology Information Vol. 1 No. 3 (2023): Volume 1 Number 3 September 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v1i3.64

Abstract

Technology develops, the world of education is of course very supportive for the progress of the world of education, in this case in the field of guidance and counseling. This led to the creation of an information counseling guidance system design at sman 01 sindang lakes. Guidance counseling is a process in which a professional provides assistance to a person or individual to overcome the problems they face. Therefore, the school is not only a place for teaching and learning, but also directly or indirectly monitors the progress of its students. However, there are still a number of obstacles that must be implemented, one of which is experienced by SM N 01 Sindang Danau which manages student guidance and counseling services. Another problem is that all records are still manual in nature, such as recording cases of students who violate, starting from filing, dates, names, types of cases to letters sent to the parents of students who are often not conveyed properly. The website-based guidance and counseling information system at sman 01 trial lake has gone through the Black Box test and got a result of 93.93% with a very decent predicate. The system facilitates counseling guidance services starting from the information obtained by the student's guardian and facilitates the performance of the guidance and counseling teacher for reporting to related parties such as the teacher's guardian and the students themselves, and will also provide counseling to students and provide direction to student guardians so that the school and student guardians play a direct role in character education, especially in the field of guidance and counseling.
Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen Auliya Rahman Isnain; Heni Sulistiani; Bagus Miftaq Hurohman; Andi Nurkholis; Styawati Styawati
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 2 (2022): Volume 8 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i2.54704

Abstract

New Normal merupakan sebuah sebutan bagi kebijakan pemerintah untuk mengizinkan masyarakatnya melakukan aktifitas seperti biasa di tengah pandemi Covid-19 yang sedang melanda dengan tetap memperhatikan protokol kesehatan. Kebijakan ini menimbulkan berbagai tanggapan dari masyarakat terutama di media sosial twitter. Untuk itu, diperlukan proses analisis sentimen untuk melakukan pemrosesan terhadap teks yang didapat dari twitter. Analisis sentimen adalah bentuk representasi dari text mining dan text processing. Pada penelitian ini melakukan perbandingan kinerja metode Long Short Therm Memory dengan Naïve Bayes terhadap analisis sentimen Kebijakan New Normal. Hasil yang diperoleh dari penelitian ini yaitu metode  LSTM memiliki kinerja yang lebih baik bila dibandingkan dengan Naïve Bayes. Metode LSTM menghasilkan nilai akurasi, presisi dan recall sebesar 83.33%. Sedangkan metode Naïve Bayes memiliki nilai akurasi, presisi dan recall sebesar 82%.
IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK UNTUK ANALISIS SENTIMEN BACAPRES 2024 PADA KOLOM KOMENTAR YOUTUBE MATA NAJWA Saputra, Dany Eka; Isnain, Auliya Rahman
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5420

Abstract

Indonesia sebagai salah satu negara berpenduduk padat dengan sistem demokrasi, Penelitian ini berfokus pada analisis sentimen terhadap calon presiden dan wakil presiden 2024 melalui komentar YouTube di "Mata Najwa." Memanfaatkan Convolutional Neural Network (CNN) pada 45.736 komentar, penelitian ini mencapai akurasi keseluruhan 91% yang mengesankan. Metode CNN, menggunakan fase arsitektur dan fine-tuning dengan pengoptimal Adam, secara efektif mengkategorikan sentimen ke dalam kelas positif, negatif, dan netral. Kemahiran model dalam menavigasi dinamika bahasa dan fluktuasi opini publik menunjukkan dampak positifnya pada tantangan analisis sentimen dalam konteks politik platform media sosial seperti YouTube. Penelitian ini menyoroti kemanjuran CNN dalam menangani seluk-beluk wacana politik dalam skala besar, menawarkan wawasan berharga tentang sentimen publik selama musim pemilihan.
Komparasi Algoritma Naïve Bayes dan Support Vector Machine (SVM) pada Analisis Sentimen Capcut Zai, Charles; Isnain, Auliya Rahman
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4054

Abstract

Capcut adalah platform pengeditan video yang dapat digunakan pada smartphone Android, PC, dan browser. Dengan fitur-fiturnya yang inovatif dan kreatif, Capcut terus berupaya untuk meningkatkan kualitas layanannya melalui update berkala. Namun, setiap perubahan tidak selalu sesuai dengan apa yang diharapkan semua pengguna. Ini mengakibatkan berbagai perspektif dan pengalaman dengan aplikasi Capcut, yang tercermin dalam ulasan pengguna di Play Store. Salah satu langkah penting untuk memahami persepsi dan pengalaman pengguna dengan Capcut adalah melaksanakan analisa sentimen. Pada analisis ini melakukan komparasi algortima naïve bayes dan SVM dengan menerapkan optimasi SMOTE. Hasil komparasi pada algoritma naïve bayes mempunyai akurasi 81% dan algoritma SVM mempunyai akurasi 86%, yang menunjukkan bahwa kedua algortima memiliki senimen positif yang lebih baik daripada sebelum menggunakan SMOTE. Disimpulkan dari kedua algoritma bahwa model Support Vector Machine (SVM) terbukti algoritma terbaik. Hasil visualisasi wordcloud sentimen positif mengacu pada kepuasan pengguna dan fitur yang disukai, sedangkan hasil visualisasi wordcloud negatif mengacu pada ketidaknyamanan pengguna dalam melakukan proses pengeditan video.
Sentimen Analisis Masyarakat Terhadap Pembangunan IKN Menggunakan Algoritma Lexicon Based Approach dan Naïve Bayes Setiawan, Samuel Budi; Isnain, Auliya Rahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7506

Abstract

The relocation and construction of IKN (Capital City of the Archipelago) as a center for state administration activities has many benefits and shortcomings, starting from the selection of locations, the ratification of laws that are considered too hasty then raises pros and cons by the Indonesian people. President Joko Widodo decided to move the country's capital outside Java in a meeting on April 29, 2019. The location of the IKN development was determined in East Kalimantan. This research was conducted by retrieving data via Twitter with the keyword "IKN Development". The data that has been collected totals 3,680 tweets. Data analysis was carried out with two methods, namely Naïve Bayes Classifier and Lexicon Based, and the best accuracy value was found between the two methods in analyzing data on public responses to IKN Development. The initial step of the data analysis process is the preprocessing process which contains stages such as labelling, case folding, cleaning, tokenizing, stopword removal, stemming. It is known that the results obtained from the analysis of the Naïve Bayes Classifier method have an accuracy value of 79%, and Lexicon Based has an accuracy value of 76%. Sentiment analysis of the two methods has Positive, Negative, and Neutral sentiments. With the stages of the analysis process using the Naïve Bayes Classifier and lexicon based methods, it can be seen that the Naïve Bayes Classifier method shows a Positive sentiment of 47.18%, Negative of 6.33%, and Neutral of 46.49%, while for Lexicon Based, Positive sentiment reaches 54.15%, Negative 29.36%, and Neutral 16.49%. It should be noted that the highest positive polarity result is found in the Lexicon Based algorithm at 54.15%, while in the Naïve Bayes Classifier 47.18%. It can be concluded from the results of both methods that Naïve Bayes Classifier has a better analysis compared to Lexicon-Based analysis.
Analisis Sentimen Opini Terhadap Tools Artificial Intelligence (AI) Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes Oktavia, Ingrid; Isnain, Auliya Rahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7524

Abstract

This research aims to analyze public sentiment towards artificial intelligence (AI) tools via the Twitter platform using the Naive Bayes classifier algorithm. Twitter is a popular social media platform for sharing opinions and thoughts, making it suitable for sentiment analysis. Sentiment analysis is the process of analyzing and understanding opinions, attitudes, or feelings contained in text, such as tweets, product reviews, or other social media posts. The problems discussed in sentiment analysis can vary depending on the context. Tests carried out using the Naïve Bayes Classifier algorithm can conclude that the data collected was 2119. In this research, there are several steps that must be taken to analyzethe data, starting with crawling, labeling, preprocessing, splitting data, dividing test data, and training data, and finally applying the Naïve Bayes Classifier Algorithm. The results of the data analysis were divided into two categories: positive and negative, with 58.41% positive data and 12.43% negative data. In the analysis experiment, the Naïve Bayes accuracy value reached 79.41%, with a precision of 88% and a recall of 88%. The aim of the results of this research is to examine the public's response regarding artificial intelligence tools using the Naïve Bayes Classifier Algorithm to provide better sentiment results. So many see AI as a technology that carries great potential to improve human life. On the other hand, there are concerns about AI's negative impact on employment, privacy, and even its potential to take over human control. Ethical concerns also arise regarding the use of AI in decision-making that can affect human lives without adequate control. So artificial intelligence tools can be accepted by society because they have many benefits. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a breakdown process.
Analisis Sentimen Masyarakat Terhadap Tiktok Shop di Twitter Menggunakan Metode Naive Bayes Classifier Andrian, Eka; Isnain, Auliya Rahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7530

Abstract

This research aims to analyze public sentiment towards TikTok Shop through the Twitter platform using the Naive Bayes Classifier Algorithm. This algorithm is used to evaluate public views regarding TikTok Shop and identify Positive and Negative sentiments. The data used in this research is 3,816 data. Then, there are Positive sentiment results of 53.45% and Negative of 46.55%. After analyzing the data, the accuracy result is 78.22% using the Split Data operator. After that, for the results of the Naïve Bayes Classifier implementation on the Recall value has a result of 84% and for the class precision result of 86%. The purpose of this research is to evaluate public views on TikTok Shop through the Twitter platform by utilizing the Naive Bayes Classifier Algorithm. This algorithm is used to analyze sentiments that arise regarding TikTok Shop, with a focus on identifying whether the sentiment is Positive or Negative. This analysis is also used to find out different public opinions about TikTok Shop, such as user experience, features used, and impacts experienced. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a splitting process.
Co-Authors Adam Indra Sakti Ade Dwi Putra Ade Surahman Adhie Thyo Priandika Ady Chandra Agung Saputra Agus Sihabuddin Ahmad Ari Aldino Aidil Akbar Alita, Debby Andi Nurkholis Andrian, Eka Aprian Nuriansah Aprilia, Ni Wayan Ayu Sekar Ari Wibowo Bagus Miftaq Hurohman Bakti Eka Putra Deby Sintia Amelia Dedi Darwis Dinda Ayu Prasticha Donaya Pasha Elisa, Putri Elvano Delisa Mega Fatriade Saputra Fenty ariany Fikri Hamidy Heni Sulistiani Ikbal Yasin Ikbal Yasin Indah Sari Jepi Supriyanto Jepi Supriyanto Lestari, Rohmah Dewi Lili Andraini Linda Yunita M Agung Saputra Mesran, Mesran Muhammad Indigo Muhammad Pajar Kharisma Muhammad Pajar Kharisma Putra Nadia Apriliza Neneng Neneng Nirwana Hendrastuty Nurman Satya Marga Nurman Satya Marga Nuroji Nuroji Oktavia, Ingrid Permata Permata Priandika, Arjuna Priskilia Lovika Prita Dellia Qadhli Jafar Adrian Rido Febriansyah Rika Mersita Romadhona, Waldy Ryan Randy Suryono Sanriomi Sintaro Saputra, Dany Eka Sebastian, Dicky Fernanda Setiawan, Samuel Budi Setiawansyah Setiawansyah Styawati Styawati Taufiq Martiwansyah Very Hendra Saputra Wahyudi, Agung Deni Wang, Junhai Yasinta Ismi Yohanes Suyanto Yunita Yunita Yuri Rahmanto Zaenal Abidin Zai, Charles Zeafli Pebriawan Zulfa Nabila